Overview

Dataset statistics

Number of variables28
Number of observations4846
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory224.0 B

Variable types

Text3
Numeric10
Categorical15

Alerts

Score is highly overall correlated with Real Guest Cleanlines Score and 5 other fieldsHigh correlation
Reviews is highly overall correlated with Booked todayHigh correlation
Booked today is highly overall correlated with ReviewsHigh correlation
Real Guest Cleanlines Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Real Guest Facilities Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Real Guest Location Score is highly overall correlated with Score and 4 other fieldsHigh correlation
Real Guest Service Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Real Guest Value for money Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Sparkling clean is highly overall correlated with Score and 4 other fieldsHigh correlation
NewlyBuilt is highly imbalanced (85.4%)Imbalance
ExcellentView is highly imbalanced (72.4%)Imbalance
Free WiFi In All Rooms is highly imbalanced (70.7%)Imbalance
Kids club is highly imbalanced (66.5%)Imbalance
Stars has 557 (11.5%) zerosZeros
Booked today has 3459 (71.4%) zerosZeros

Reproduction

Analysis started2023-06-05 15:06:58.177934
Analysis finished2023-06-05 15:07:50.135445
Duration51.96 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Name
Text

Distinct4299
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-06-05T18:07:51.523291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length100
Median length65
Mean length25.549938
Min length3

Characters and Unicode

Total characters123815
Distinct characters257
Distinct categories14 ?
Distinct scripts7 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3794 ?
Unique (%)78.3%

Sample

1st rowAsia Hotel Bangkok (SHA Plus+)
2nd rowRembrandt Hotel & Suites (SHA Plus+)
3rd rowDream Hotel Bangkok (SHA Plus+)
4th rowVIX Bangkok @ Victory Monument
5th rowThe Berkeley Hotel Pratunam (SHA Plus+)
ValueCountFrequency (%)
hotel 1302
 
6.4%
sha 1126
 
5.6%
plus 1036
 
5.1%
resort 625
 
3.1%
bangkok 532
 
2.6%
phuket 498
 
2.5%
the 473
 
2.3%
extra 464
 
2.3%
333
 
1.6%
hostel 309
 
1.5%
Other values (3807) 13507
66.8%
2023-06-05T18:07:53.221723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15359
 
12.4%
e 9721
 
7.9%
a 9320
 
7.5%
o 7204
 
5.8%
t 6949
 
5.6%
n 5705
 
4.6%
l 5345
 
4.3%
i 4648
 
3.8%
s 4587
 
3.7%
u 4377
 
3.5%
Other values (247) 50600
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79480
64.2%
Uppercase Letter 23503
 
19.0%
Space Separator 15359
 
12.4%
Decimal Number 1342
 
1.1%
Close Punctuation 1142
 
0.9%
Open Punctuation 1140
 
0.9%
Math Symbol 559
 
0.5%
Other Punctuation 511
 
0.4%
Other Letter 468
 
0.4%
Dash Punctuation 210
 
0.2%
Other values (4) 101
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
30
 
6.4%
29
 
6.2%
24
 
5.1%
23
 
4.9%
22
 
4.7%
15
 
3.2%
15
 
3.2%
14
 
3.0%
13
 
2.8%
13
 
2.8%
Other values (111) 270
57.7%
Lowercase Letter
ValueCountFrequency (%)
e 9721
12.2%
a 9320
11.7%
o 7204
9.1%
t 6949
8.7%
n 5705
 
7.2%
l 5345
 
6.7%
i 4648
 
5.8%
s 4587
 
5.8%
u 4377
 
5.5%
r 4307
 
5.4%
Other values (46) 17317
21.8%
Uppercase Letter
ValueCountFrequency (%)
H 3499
14.9%
P 2967
12.6%
S 2791
11.9%
A 2089
8.9%
B 1948
 
8.3%
R 1571
 
6.7%
T 1167
 
5.0%
E 885
 
3.8%
C 841
 
3.6%
M 668
 
2.8%
Other values (20) 5077
21.6%
Other Punctuation
ValueCountFrequency (%)
& 187
36.6%
@ 114
22.3%
. 70
 
13.7%
' 59
 
11.5%
, 46
 
9.0%
/ 10
 
2.0%
! 8
 
1.6%
: 6
 
1.2%
6
 
1.2%
# 3
 
0.6%
Other values (2) 2
 
0.4%
Nonspacing Mark
ValueCountFrequency (%)
18
20.7%
17
19.5%
14
16.1%
11
12.6%
9
10.3%
5
 
5.7%
4
 
4.6%
4
 
4.6%
2
 
2.3%
1
 
1.1%
Other values (2) 2
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 265
19.7%
2 236
17.6%
3 143
10.7%
4 141
10.5%
8 112
8.3%
5 110
8.2%
9 94
 
7.0%
7 85
 
6.3%
0 84
 
6.3%
6 72
 
5.4%
Close Punctuation
ValueCountFrequency (%)
) 1128
98.8%
] 9
 
0.8%
3
 
0.3%
2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 1126
98.8%
[ 9
 
0.8%
3
 
0.3%
2
 
0.2%
Math Symbol
ValueCountFrequency (%)
+ 558
99.8%
~ 1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 206
98.1%
4
 
1.9%
Space Separator
ValueCountFrequency (%)
15359
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Final Punctuation
ValueCountFrequency (%)
5
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102839
83.1%
Common 20277
 
16.4%
Thai 421
 
0.3%
Cyrillic 144
 
0.1%
Han 114
 
0.1%
Arabic 19
 
< 0.1%
Inherited 1
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
6
 
5.3%
6
 
5.3%
4
 
3.5%
4
 
3.5%
4
 
3.5%
3
 
2.6%
3
 
2.6%
3
 
2.6%
2
 
1.8%
2
 
1.8%
Other values (64) 77
67.5%
Latin
ValueCountFrequency (%)
e 9721
 
9.5%
a 9320
 
9.1%
o 7204
 
7.0%
t 6949
 
6.8%
n 5705
 
5.5%
l 5345
 
5.2%
i 4648
 
4.5%
s 4587
 
4.5%
u 4377
 
4.3%
r 4307
 
4.2%
Other values (47) 40676
39.6%
Thai
ValueCountFrequency (%)
30
 
7.1%
29
 
6.9%
24
 
5.7%
23
 
5.5%
22
 
5.2%
18
 
4.3%
17
 
4.0%
15
 
3.6%
15
 
3.6%
14
 
3.3%
Other values (35) 214
50.8%
Common
ValueCountFrequency (%)
15359
75.7%
) 1128
 
5.6%
( 1126
 
5.6%
+ 558
 
2.8%
1 265
 
1.3%
2 236
 
1.2%
- 206
 
1.0%
& 187
 
0.9%
3 143
 
0.7%
4 141
 
0.7%
Other values (28) 928
 
4.6%
Cyrillic
ValueCountFrequency (%)
а 20
13.9%
н 13
 
9.0%
е 12
 
8.3%
т 9
 
6.2%
о 9
 
6.2%
в 8
 
5.6%
л 8
 
5.6%
п 7
 
4.9%
ы 6
 
4.2%
м 6
 
4.2%
Other values (19) 46
31.9%
Arabic
ValueCountFrequency (%)
ا 4
21.1%
ل 2
10.5%
ر 2
10.5%
ع 2
10.5%
ب 1
 
5.3%
ش 1
 
5.3%
ة 1
 
5.3%
س 1
 
5.3%
م 1
 
5.3%
ق 1
 
5.3%
Other values (3) 3
15.8%
Inherited
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123081
99.4%
Thai 421
 
0.3%
Cyrillic 144
 
0.1%
CJK 114
 
0.1%
None 19
 
< 0.1%
Arabic 19
 
< 0.1%
Punctuation 15
 
< 0.1%
VS 1
 
< 0.1%
Dingbats 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15359
 
12.5%
e 9721
 
7.9%
a 9320
 
7.6%
o 7204
 
5.9%
t 6949
 
5.6%
n 5705
 
4.6%
l 5345
 
4.3%
i 4648
 
3.8%
s 4587
 
3.7%
u 4377
 
3.6%
Other values (71) 49866
40.5%
Thai
ValueCountFrequency (%)
30
 
7.1%
29
 
6.9%
24
 
5.7%
23
 
5.5%
22
 
5.2%
18
 
4.3%
17
 
4.0%
15
 
3.6%
15
 
3.6%
14
 
3.3%
Other values (35) 214
50.8%
Cyrillic
ValueCountFrequency (%)
а 20
13.9%
н 13
 
9.0%
е 12
 
8.3%
т 9
 
6.2%
о 9
 
6.2%
в 8
 
5.6%
л 8
 
5.6%
п 7
 
4.9%
ы 6
 
4.2%
м 6
 
4.2%
Other values (19) 46
31.9%
Punctuation
ValueCountFrequency (%)
6
40.0%
5
33.3%
4
26.7%
CJK
ValueCountFrequency (%)
6
 
5.3%
6
 
5.3%
4
 
3.5%
4
 
3.5%
4
 
3.5%
3
 
2.6%
3
 
2.6%
3
 
2.6%
2
 
1.8%
2
 
1.8%
Other values (64) 77
67.5%
None
ValueCountFrequency (%)
ö 4
21.1%
3
15.8%
3
15.8%
2
10.5%
2
10.5%
â 1
 
5.3%
é 1
 
5.3%
1
 
5.3%
à 1
 
5.3%
è 1
 
5.3%
Arabic
ValueCountFrequency (%)
ا 4
21.1%
ل 2
10.5%
ر 2
10.5%
ع 2
10.5%
ب 1
 
5.3%
ش 1
 
5.3%
ة 1
 
5.3%
س 1
 
5.3%
م 1
 
5.3%
ق 1
 
5.3%
Other values (3) 3
15.8%
VS
ValueCountFrequency (%)
1
100.0%
Dingbats
ValueCountFrequency (%)
1
100.0%

Price
Real number (ℝ)

Distinct526
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.94305
Minimum12
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:07:53.723937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile45
Q169
median96
Q3155
95-th percentile478
Maximum999
Range987
Interquartile range (IQR)86

Descriptive statistics

Standard deviation146.96549
Coefficient of variation (CV)0.99339234
Kurtosis9.3295481
Mean147.94305
Median Absolute Deviation (MAD)35
Skewness2.8814196
Sum716932
Variance21598.855
MonotonicityNot monotonic
2023-06-05T18:07:54.068210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 114
 
2.4%
69 106
 
2.2%
89 89
 
1.8%
99 87
 
1.8%
59 65
 
1.3%
49 63
 
1.3%
73 62
 
1.3%
72 61
 
1.3%
66 57
 
1.2%
90 56
 
1.2%
Other values (516) 4086
84.3%
ValueCountFrequency (%)
12 1
 
< 0.1%
19 1
 
< 0.1%
21 2
 
< 0.1%
22 2
 
< 0.1%
24 2
 
< 0.1%
25 1
 
< 0.1%
27 3
 
0.1%
28 2
 
< 0.1%
29 2
 
< 0.1%
30 8
0.2%
ValueCountFrequency (%)
999 1
 
< 0.1%
995 1
 
< 0.1%
989 1
 
< 0.1%
987 1
 
< 0.1%
975 1
 
< 0.1%
968 3
0.1%
967 1
 
< 0.1%
965 1
 
< 0.1%
964 1
 
< 0.1%
959 1
 
< 0.1%

Stars
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8645274
Minimum0
Maximum5
Zeros557
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:07:54.323710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3576837
Coefficient of variation (CV)0.47396427
Kurtosis0.032416225
Mean2.8645274
Median Absolute Deviation (MAD)1
Skewness-0.65928595
Sum13881.5
Variance1.8433049
MonotonicityNot monotonic
2023-06-05T18:07:54.521671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 1549
32.0%
4 821
16.9%
2 687
14.2%
0 557
 
11.5%
5 411
 
8.5%
2.5 284
 
5.9%
3.5 256
 
5.3%
4.5 173
 
3.6%
1.5 58
 
1.2%
1 50
 
1.0%
ValueCountFrequency (%)
0 557
 
11.5%
1 50
 
1.0%
1.5 58
 
1.2%
2 687
14.2%
2.5 284
 
5.9%
3 1549
32.0%
3.5 256
 
5.3%
4 821
16.9%
4.5 173
 
3.6%
5 411
 
8.5%
ValueCountFrequency (%)
5 411
 
8.5%
4.5 173
 
3.6%
4 821
16.9%
3.5 256
 
5.3%
3 1549
32.0%
2.5 284
 
5.9%
2 687
14.2%
1.5 58
 
1.2%
1 50
 
1.0%
0 557
 
11.5%

Score
Real number (ℝ)

Distinct69
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1342757
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:07:54.763068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.5
Q17.7
median8.3
Q38.7
95-th percentile9.4
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93956677
Coefficient of variation (CV)0.11550712
Kurtosis5.1295997
Mean8.1342757
Median Absolute Deviation (MAD)0.5
Skewness-1.4293153
Sum39418.7
Variance0.88278572
MonotonicityNot monotonic
2023-06-05T18:07:55.369364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4 305
 
6.3%
8.5 255
 
5.3%
8.3 240
 
5.0%
8.8 233
 
4.8%
8.7 232
 
4.8%
8.6 231
 
4.8%
8.1 230
 
4.7%
7.9 225
 
4.6%
8.2 221
 
4.6%
8 211
 
4.4%
Other values (59) 2463
50.8%
ValueCountFrequency (%)
2 2
 
< 0.1%
2.3 5
0.1%
2.5 1
 
< 0.1%
2.8 2
 
< 0.1%
3 2
 
< 0.1%
3.1 4
0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.6 3
0.1%
3.9 1
 
< 0.1%
ValueCountFrequency (%)
10 53
1.1%
9.9 12
 
0.2%
9.8 32
 
0.7%
9.7 16
 
0.3%
9.6 54
1.1%
9.5 54
1.1%
9.4 56
1.2%
9.3 81
1.7%
9.2 127
2.6%
9.1 125
2.6%
Distinct473
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-06-05T18:07:55.820357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length389
Median length13
Mean length21.577383
Min length4

Characters and Unicode

Total characters104564
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique294 ?
Unique (%)6.1%

Sample

1st rowEnglish, Thai
2nd rowEnglish, Thai
3rd rowEnglish, Thai
4th rowEnglish, Chinese [Mandarin], Thai
5th rowEnglish, Chinese [Mandarin], Thai
ValueCountFrequency (%)
english 4709
33.2%
thai 4674
33.0%
chinese 1027
 
7.3%
mandarin 892
 
6.3%
french 356
 
2.5%
german 250
 
1.8%
russian 229
 
1.6%
burmese 226
 
1.6%
japanese 222
 
1.6%
hindi 168
 
1.2%
Other values (35) 1410
 
10.0%
2023-06-05T18:07:56.619613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 13058
12.5%
h 11130
10.6%
n 9825
9.4%
9317
8.9%
a 8656
 
8.3%
, 8290
 
7.9%
s 7130
 
6.8%
l 5155
 
4.9%
g 4761
 
4.6%
T 4715
 
4.5%
Other values (36) 22527
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70740
67.7%
Uppercase Letter 14163
 
13.5%
Space Separator 9317
 
8.9%
Other Punctuation 8290
 
7.9%
Open Punctuation 1027
 
1.0%
Close Punctuation 1027
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 13058
18.5%
h 11130
15.7%
n 9825
13.9%
a 8656
12.2%
s 7130
10.1%
l 5155
 
7.3%
g 4761
 
6.7%
e 4254
 
6.0%
r 2033
 
2.9%
d 1143
 
1.6%
Other values (12) 3595
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
T 4715
33.3%
E 4715
33.3%
C 1181
 
8.3%
M 964
 
6.8%
F 529
 
3.7%
G 257
 
1.8%
B 233
 
1.6%
R 231
 
1.6%
J 222
 
1.6%
H 188
 
1.3%
Other values (10) 928
 
6.6%
Space Separator
ValueCountFrequency (%)
9317
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8290
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1027
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1027
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84903
81.2%
Common 19661
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 13058
15.4%
h 11130
13.1%
n 9825
11.6%
a 8656
10.2%
s 7130
8.4%
l 5155
 
6.1%
g 4761
 
5.6%
T 4715
 
5.6%
E 4715
 
5.6%
e 4254
 
5.0%
Other values (32) 11504
13.5%
Common
ValueCountFrequency (%)
9317
47.4%
, 8290
42.2%
[ 1027
 
5.2%
] 1027
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 13058
12.5%
h 11130
10.6%
n 9825
9.4%
9317
8.9%
a 8656
 
8.3%
, 8290
 
7.9%
s 7130
 
6.8%
l 5155
 
4.9%
g 4761
 
4.6%
T 4715
 
4.5%
Other values (36) 22527
21.5%

Reviews
Real number (ℝ)

Distinct1733
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean983.26352
Minimum1
Maximum61617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:07:56.900862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q159
median265.5
Q3990.5
95-th percentile4108.5
Maximum61617
Range61616
Interquartile range (IQR)931.5

Descriptive statistics

Standard deviation2269.4662
Coefficient of variation (CV)2.3080956
Kurtosis156.07485
Mean983.26352
Median Absolute Deviation (MAD)249
Skewness9.1368644
Sum4764895
Variance5150477
MonotonicityNot monotonic
2023-06-05T18:07:57.161184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 98
 
2.0%
2 86
 
1.8%
3 56
 
1.2%
5 48
 
1.0%
4 42
 
0.9%
8 36
 
0.7%
9 34
 
0.7%
6 32
 
0.7%
10 29
 
0.6%
22 29
 
0.6%
Other values (1723) 4356
89.9%
ValueCountFrequency (%)
1 98
2.0%
2 86
1.8%
3 56
1.2%
4 42
0.9%
5 48
1.0%
6 32
 
0.7%
7 24
 
0.5%
8 36
 
0.7%
9 34
 
0.7%
10 29
 
0.6%
ValueCountFrequency (%)
61617 1
< 0.1%
40320 1
< 0.1%
28839 1
< 0.1%
28073 1
< 0.1%
27771 2
< 0.1%
25454 1
< 0.1%
24128 1
< 0.1%
23462 1
< 0.1%
22016 1
< 0.1%
20544 1
< 0.1%
Distinct3812
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-06-05T18:07:57.884842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length236
Median length136
Mean length71.932934
Min length22

Characters and Unicode

Total characters348587
Distinct characters153
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3298 ?
Unique (%)68.1%

Sample

1st row296 Phayathai Road, Siam, Bangkok, Thailand, 10400
2nd row19 Sukhumvit Soi 18, Klong Toei, Sukhumvit, Bangkok, Thailand, 10110
3rd row10 Sukhumvit Soi 15, Sukhumvit, Bangkok, Thailand, 10110
4th row13-15 Thanon Ratchawithi, Chatuchak, Bangkok, Thailand, 10400
5th row559 Ratchathewi, Pratunam, Bangkok, Thailand, 10400
ValueCountFrequency (%)
thailand 5056
 
10.3%
bangkok 3006
 
6.1%
phuket 2933
 
6.0%
road 1653
 
3.4%
soi 1475
 
3.0%
sukhumvit 1026
 
2.1%
patong 819
 
1.7%
rd 688
 
1.4%
83150 688
 
1.4%
moo 600
 
1.2%
Other values (5543) 31155
63.5%
2023-06-05T18:07:58.934494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44253
 
12.7%
a 37257
 
10.7%
, 24212
 
6.9%
n 20373
 
5.8%
h 16977
 
4.9%
o 16413
 
4.7%
i 14383
 
4.1%
k 12404
 
3.6%
t 11840
 
3.4%
0 10313
 
3.0%
Other values (143) 140162
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 192567
55.2%
Space Separator 44253
 
12.7%
Decimal Number 41370
 
11.9%
Uppercase Letter 37994
 
10.9%
Other Punctuation 27803
 
8.0%
Other Letter 2490
 
0.7%
Dash Punctuation 1249
 
0.4%
Nonspacing Mark 405
 
0.1%
Open Punctuation 221
 
0.1%
Close Punctuation 220
 
0.1%
Other values (4) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
221
 
8.9%
213
 
8.6%
174
 
7.0%
162
 
6.5%
145
 
5.8%
116
 
4.7%
109
 
4.4%
104
 
4.2%
94
 
3.8%
91
 
3.7%
Other values (47) 1061
42.6%
Uppercase Letter
ValueCountFrequency (%)
T 7458
19.6%
P 6342
16.7%
S 4526
11.9%
B 4447
11.7%
R 3824
10.1%
K 2957
 
7.8%
M 1772
 
4.7%
N 1168
 
3.1%
A 1149
 
3.0%
C 942
 
2.5%
Other values (17) 3409
9.0%
Lowercase Letter
ValueCountFrequency (%)
a 37257
19.3%
n 20373
10.6%
h 16977
8.8%
o 16413
8.5%
i 14383
 
7.5%
k 12404
 
6.4%
t 11840
 
6.1%
u 9110
 
4.7%
d 8913
 
4.6%
g 8440
 
4.4%
Other values (16) 36457
18.9%
Decimal Number
ValueCountFrequency (%)
0 10313
24.9%
1 9864
23.8%
3 4453
10.8%
8 3772
 
9.1%
2 3736
 
9.0%
5 2599
 
6.3%
4 2262
 
5.5%
6 1592
 
3.8%
9 1468
 
3.5%
7 1310
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 24212
87.1%
/ 2379
 
8.6%
. 1186
 
4.3%
& 12
 
< 0.1%
; 4
 
< 0.1%
# 3
 
< 0.1%
: 3
 
< 0.1%
@ 2
 
< 0.1%
* 1
 
< 0.1%
، 1
 
< 0.1%
Nonspacing Mark
ValueCountFrequency (%)
60
14.8%
54
13.3%
50
12.3%
46
11.4%
45
11.1%
42
10.4%
40
9.9%
32
7.9%
23
 
5.7%
13
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 1247
99.8%
2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 219
99.1%
[ 2
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 218
99.1%
] 2
 
0.9%
Math Symbol
ValueCountFrequency (%)
+ 6
60.0%
| 4
40.0%
Space Separator
ValueCountFrequency (%)
44253
100.0%
Currency Symbol
ValueCountFrequency (%)
2
100.0%
Initial Punctuation
ValueCountFrequency (%)
2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230561
66.1%
Common 115130
33.0%
Thai 2888
 
0.8%
Arabic 8
 
< 0.1%

Most frequent character per script

Thai
ValueCountFrequency (%)
221
 
7.7%
213
 
7.4%
174
 
6.0%
162
 
5.6%
145
 
5.0%
116
 
4.0%
109
 
3.8%
104
 
3.6%
94
 
3.3%
91
 
3.2%
Other values (50) 1459
50.5%
Latin
ValueCountFrequency (%)
a 37257
16.2%
n 20373
 
8.8%
h 16977
 
7.4%
o 16413
 
7.1%
i 14383
 
6.2%
k 12404
 
5.4%
t 11840
 
5.1%
u 9110
 
4.0%
d 8913
 
3.9%
g 8440
 
3.7%
Other values (43) 74451
32.3%
Common
ValueCountFrequency (%)
44253
38.4%
, 24212
21.0%
0 10313
 
9.0%
1 9864
 
8.6%
3 4453
 
3.9%
8 3772
 
3.3%
2 3736
 
3.2%
5 2599
 
2.3%
/ 2379
 
2.1%
4 2262
 
2.0%
Other values (22) 7287
 
6.3%
Arabic
ValueCountFrequency (%)
ة 1
12.5%
س 1
12.5%
م 1
12.5%
ا 1
12.5%
ق 1
12.5%
د 1
12.5%
ن 1
12.5%
ف 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 345681
99.2%
Thai 2888
 
0.8%
Arabic 9
 
< 0.1%
Punctuation 5
 
< 0.1%
Currency Symbols 2
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44253
 
12.8%
a 37257
 
10.8%
, 24212
 
7.0%
n 20373
 
5.9%
h 16977
 
4.9%
o 16413
 
4.7%
i 14383
 
4.2%
k 12404
 
3.6%
t 11840
 
3.4%
0 10313
 
3.0%
Other values (69) 137256
39.7%
Thai
ValueCountFrequency (%)
221
 
7.7%
213
 
7.4%
174
 
6.0%
162
 
5.6%
145
 
5.0%
116
 
4.0%
109
 
3.8%
104
 
3.6%
94
 
3.3%
91
 
3.2%
Other values (50) 1459
50.5%
Currency Symbols
ValueCountFrequency (%)
2
100.0%
Punctuation
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%
None
ValueCountFrequency (%)
 2
100.0%
Arabic
ValueCountFrequency (%)
ة 1
11.1%
س 1
11.1%
م 1
11.1%
ا 1
11.1%
ق 1
11.1%
د 1
11.1%
ن 1
11.1%
ف 1
11.1%
، 1
11.1%

Sparkling clean
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
3486 
1
1360 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3486
71.9%
1 1360
 
28.1%

Length

2023-06-05T18:07:59.192455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:07:59.443131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3486
71.9%
1 1360
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 3486
71.9%
1 1360
 
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3486
71.9%
1 1360
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3486
71.9%
1 1360
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3486
71.9%
1 1360
 
28.1%

NewlyBuilt
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4745 
1
 
101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4745
97.9%
1 101
 
2.1%

Length

2023-06-05T18:07:59.617715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:07:59.807423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4745
97.9%
1 101
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 4745
97.9%
1 101
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4745
97.9%
1 101
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4745
97.9%
1 101
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4745
97.9%
1 101
 
2.1%

ExcellentView
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4616 
1
 
230

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4616
95.3%
1 230
 
4.7%

Length

2023-06-05T18:07:59.968499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:00.229824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4616
95.3%
1 230
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 4616
95.3%
1 230
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4616
95.3%
1 230
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4616
95.3%
1 230
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4616
95.3%
1 230
 
4.7%

Check In 24/7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
2970 
1
1876 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2970
61.3%
1 1876
38.7%

Length

2023-06-05T18:08:00.469832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:00.805809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2970
61.3%
1 1876
38.7%

Most occurring characters

ValueCountFrequency (%)
0 2970
61.3%
1 1876
38.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2970
61.3%
1 1876
38.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2970
61.3%
1 1876
38.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2970
61.3%
1 1876
38.7%

AirportTransfer
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
2672 
1
2174 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2672
55.1%
1 2174
44.9%

Length

2023-06-05T18:08:01.057114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:01.291609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2672
55.1%
1 2174
44.9%

Most occurring characters

ValueCountFrequency (%)
0 2672
55.1%
1 2174
44.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2672
55.1%
1 2174
44.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2672
55.1%
1 2174
44.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2672
55.1%
1 2174
44.9%

Front Desk
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
1
3008 
0
1838 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3008
62.1%
0 1838
37.9%

Length

2023-06-05T18:08:01.480176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:01.686585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3008
62.1%
0 1838
37.9%

Most occurring characters

ValueCountFrequency (%)
1 3008
62.1%
0 1838
37.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3008
62.1%
0 1838
37.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3008
62.1%
0 1838
37.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3008
62.1%
0 1838
37.9%

Valet Parking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4123 
1
723 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4123
85.1%
1 723
 
14.9%

Length

2023-06-05T18:08:01.860140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:02.047135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4123
85.1%
1 723
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 4123
85.1%
1 723
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4123
85.1%
1 723
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4123
85.1%
1 723
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4123
85.1%
1 723
 
14.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
🌐
4596 
❌🌐
 
250

Length

Max length2
Median length1
Mean length1.0515889
Min length1

Characters and Unicode

Total characters5096
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row🌐
2nd row🌐
3rd row🌐
4th row🌐
5th row🌐

Common Values

ValueCountFrequency (%)
🌐 4596
94.8%
❌🌐 250
 
5.2%

Length

2023-06-05T18:08:02.241673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:02.437358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
🌐 4596
94.8%
❌🌐 250
 
5.2%

Most occurring characters

ValueCountFrequency (%)
🌐 4846
95.1%
250
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Other Symbol 5096
100.0%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
🌐 4846
95.1%
250
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
🌐 4846
95.1%
250
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
None 4846
95.1%
Dingbats 250
 
4.9%

Most frequent character per block

None
ValueCountFrequency (%)
🌐 4846
100.0%
Dingbats
ValueCountFrequency (%)
250
100.0%

Swimming Pool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
3706 
1
1140 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3706
76.5%
1 1140
 
23.5%

Length

2023-06-05T18:08:02.598252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:02.783628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3706
76.5%
1 1140
 
23.5%

Most occurring characters

ValueCountFrequency (%)
0 3706
76.5%
1 1140
 
23.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3706
76.5%
1 1140
 
23.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3706
76.5%
1 1140
 
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3706
76.5%
1 1140
 
23.5%

Bar
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
2712 
1
2134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2712
56.0%
1 2134
44.0%

Length

2023-06-05T18:08:02.942532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:03.128001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2712
56.0%
1 2134
44.0%

Most occurring characters

ValueCountFrequency (%)
0 2712
56.0%
1 2134
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2712
56.0%
1 2134
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2712
56.0%
1 2134
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2712
56.0%
1 2134
44.0%

Coffee
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
1
2811 
0
2035 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2811
58.0%
0 2035
42.0%

Length

2023-06-05T18:08:03.299572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:03.484097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2811
58.0%
0 2035
42.0%

Most occurring characters

ValueCountFrequency (%)
1 2811
58.0%
0 2035
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2811
58.0%
0 2035
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2811
58.0%
0 2035
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2811
58.0%
0 2035
42.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
1
3980 
0
866 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3980
82.1%
0 866
 
17.9%

Length

2023-06-05T18:08:03.646286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:03.842227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3980
82.1%
0 866
 
17.9%

Most occurring characters

ValueCountFrequency (%)
1 3980
82.1%
0 866
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3980
82.1%
0 866
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3980
82.1%
0 866
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3980
82.1%
0 866
 
17.9%

Golf
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4073 
1
773 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4073
84.0%
1 773
 
16.0%

Length

2023-06-05T18:08:04.040354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:04.287377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4073
84.0%
1 773
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 4073
84.0%
1 773
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4073
84.0%
1 773
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4073
84.0%
1 773
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4073
84.0%
1 773
 
16.0%

Kids club
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4546 
1
 
300

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4846
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4546
93.8%
1 300
 
6.2%

Length

2023-06-05T18:08:04.460809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:04.652268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4546
93.8%
1 300
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 4546
93.8%
1 300
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4846
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4546
93.8%
1 300
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4846
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4546
93.8%
1 300
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4546
93.8%
1 300
 
6.2%

Booked today
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6318613
Minimum0
Maximum176
Zeros3459
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:08:04.968158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile20
Maximum176
Range176
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.4795771
Coefficient of variation (CV)2.6101154
Kurtosis71.720423
Mean3.6318613
Median Absolute Deviation (MAD)0
Skewness6.332996
Sum17600
Variance89.862382
MonotonicityNot monotonic
2023-06-05T18:08:05.694805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3459
71.4%
3 190
 
3.9%
4 155
 
3.2%
5 121
 
2.5%
6 105
 
2.2%
7 78
 
1.6%
8 67
 
1.4%
9 62
 
1.3%
10 55
 
1.1%
11 47
 
1.0%
Other values (55) 507
 
10.5%
ValueCountFrequency (%)
0 3459
71.4%
3 190
 
3.9%
4 155
 
3.2%
5 121
 
2.5%
6 105
 
2.2%
7 78
 
1.6%
8 67
 
1.4%
9 62
 
1.3%
10 55
 
1.1%
11 47
 
1.0%
ValueCountFrequency (%)
176 2
< 0.1%
128 1
 
< 0.1%
124 1
 
< 0.1%
109 1
 
< 0.1%
99 1
 
< 0.1%
87 1
 
< 0.1%
82 1
 
< 0.1%
74 1
 
< 0.1%
70 3
0.1%
68 3
0.1%
Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2641766
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:08:06.425118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.125
Q17.7
median8.5
Q39
95-th percentile9.8
Maximum10
Range8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.1730427
Coefficient of variation (CV)0.14194308
Kurtosis5.0945181
Mean8.2641766
Median Absolute Deviation (MAD)0.6
Skewness-1.6650898
Sum40048.2
Variance1.3760291
MonotonicityNot monotonic
2023-06-05T18:08:06.822163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.8 247
 
5.1%
9 225
 
4.6%
8.7 215
 
4.4%
8 204
 
4.2%
8.9 199
 
4.1%
8.3 199
 
4.1%
8.2 197
 
4.1%
10 196
 
4.0%
8.6 195
 
4.0%
9.1 193
 
4.0%
Other values (58) 2776
57.3%
ValueCountFrequency (%)
2 16
0.3%
2.2 2
 
< 0.1%
2.3 2
 
< 0.1%
2.5 14
0.3%
2.7 1
 
< 0.1%
3.3 2
 
< 0.1%
3.8 1
 
< 0.1%
3.9 2
 
< 0.1%
4 18
0.4%
4.2 2
 
< 0.1%
ValueCountFrequency (%)
10 196
4.0%
9.9 22
 
0.5%
9.8 38
 
0.8%
9.7 60
 
1.2%
9.6 87
1.8%
9.5 107
2.2%
9.4 129
2.7%
9.3 148
3.1%
9.2 181
3.7%
9.1 193
4.0%
Distinct71
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7709038
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:08:07.171266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.8
Q17.2
median7.9
Q38.5
95-th percentile9.4
Maximum10
Range8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2020792
Coefficient of variation (CV)0.15468975
Kurtosis3.3904577
Mean7.7709038
Median Absolute Deviation (MAD)0.7
Skewness-1.2157278
Sum37657.8
Variance1.4449943
MonotonicityNot monotonic
2023-06-05T18:08:07.488030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 234
 
4.8%
8.2 225
 
4.6%
8.1 200
 
4.1%
8.3 198
 
4.1%
7.5 196
 
4.0%
7.8 191
 
3.9%
8.5 187
 
3.9%
7.9 179
 
3.7%
8.4 179
 
3.7%
7.3 165
 
3.4%
Other values (61) 2892
59.7%
ValueCountFrequency (%)
2 17
0.4%
2.5 18
0.4%
2.7 2
 
< 0.1%
3 5
 
0.1%
3.3 8
0.2%
3.4 2
 
< 0.1%
3.5 3
 
0.1%
3.6 1
 
< 0.1%
3.7 1
 
< 0.1%
3.8 1
 
< 0.1%
ValueCountFrequency (%)
10 143
3.0%
9.9 1
 
< 0.1%
9.8 7
 
0.1%
9.7 17
 
0.4%
9.6 24
 
0.5%
9.5 42
 
0.9%
9.4 43
 
0.9%
9.3 73
1.5%
9.2 66
1.4%
9.1 68
1.4%

Real Guest Location Score
Real number (ℝ)

Distinct67
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1257532
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:08:07.795724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.4
Q17.6
median8.3
Q38.8
95-th percentile9.5
Maximum10
Range8
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.0665731
Coefficient of variation (CV)0.13125836
Kurtosis5.9241164
Mean8.1257532
Median Absolute Deviation (MAD)0.6
Skewness-1.6264057
Sum39377.4
Variance1.1375781
MonotonicityNot monotonic
2023-06-05T18:08:08.084177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 268
 
5.5%
8.4 244
 
5.0%
8.8 229
 
4.7%
8.3 226
 
4.7%
8.5 221
 
4.6%
8.6 211
 
4.4%
8.7 205
 
4.2%
8.1 200
 
4.1%
8.2 198
 
4.1%
8.9 191
 
3.9%
Other values (57) 2653
54.7%
ValueCountFrequency (%)
2 12
0.2%
2.5 14
0.3%
2.7 1
 
< 0.1%
3 7
0.1%
3.2 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
0.1%
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4 9
0.2%
ValueCountFrequency (%)
10 140
2.9%
9.9 6
 
0.1%
9.8 16
 
0.3%
9.7 19
 
0.4%
9.6 42
 
0.9%
9.5 62
1.3%
9.4 72
1.5%
9.3 115
2.4%
9.2 140
2.9%
9.1 138
2.8%

Real Guest Service Score
Real number (ℝ)

Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1960792
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:08:08.389863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q17.6
median8.4
Q39
95-th percentile9.9
Maximum10
Range8
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.2184279
Coefficient of variation (CV)0.14865985
Kurtosis3.9100083
Mean8.1960792
Median Absolute Deviation (MAD)0.7
Skewness-1.4474368
Sum39718.2
Variance1.4845667
MonotonicityNot monotonic
2023-06-05T18:08:08.673247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 237
 
4.9%
8 229
 
4.7%
8.9 208
 
4.3%
8.7 208
 
4.3%
9 206
 
4.3%
8.8 197
 
4.1%
8.6 189
 
3.9%
8.3 188
 
3.9%
8.5 186
 
3.8%
9.2 171
 
3.5%
Other values (58) 2827
58.3%
ValueCountFrequency (%)
2 16
0.3%
2.5 14
0.3%
2.7 3
 
0.1%
3 1
 
< 0.1%
3.3 2
 
< 0.1%
3.5 3
 
0.1%
3.8 4
 
0.1%
4 25
0.5%
4.1 1
 
< 0.1%
4.2 1
 
< 0.1%
ValueCountFrequency (%)
10 237
4.9%
9.9 15
 
0.3%
9.8 52
 
1.1%
9.7 58
 
1.2%
9.6 78
 
1.6%
9.5 87
 
1.8%
9.4 109
2.2%
9.3 165
3.4%
9.2 171
3.5%
9.1 145
3.0%
Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2988238
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-05T18:08:08.969574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.3
Q17.8
median8.5
Q39
95-th percentile9.9
Maximum10
Range8
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.1305882
Coefficient of variation (CV)0.13623476
Kurtosis6.2067719
Mean8.2988238
Median Absolute Deviation (MAD)0.6
Skewness-1.7851514
Sum40216.1
Variance1.2782298
MonotonicityNot monotonic
2023-06-05T18:08:09.461952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.8 296
 
6.1%
9 237
 
4.9%
8.9 222
 
4.6%
8.6 221
 
4.6%
10 217
 
4.5%
8.4 211
 
4.4%
8.5 207
 
4.3%
8 205
 
4.2%
8.7 196
 
4.0%
8.3 196
 
4.0%
Other values (58) 2638
54.4%
ValueCountFrequency (%)
2 18
0.4%
2.1 2
 
< 0.1%
2.5 11
0.2%
3 2
 
< 0.1%
3.2 1
 
< 0.1%
3.3 3
 
0.1%
3.5 1
 
< 0.1%
3.8 3
 
0.1%
3.9 1
 
< 0.1%
4 17
0.4%
ValueCountFrequency (%)
10 217
4.5%
9.9 35
 
0.7%
9.8 53
 
1.1%
9.7 48
 
1.0%
9.6 74
 
1.5%
9.5 78
 
1.6%
9.4 103
2.1%
9.3 115
2.4%
9.2 176
3.6%
9.1 175
3.6%

Origin
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
Bangkok
2487 
Phuket
2132 
Ko Pha-ngan
 
115
Ko Phi Phi
 
111
Koh Samui
 
1

Length

Max length11
Median length7
Mean length6.7241024
Min length6

Characters and Unicode

Total characters32585
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBangkok
2nd rowBangkok
3rd rowBangkok
4th rowBangkok
5th rowBangkok

Common Values

ValueCountFrequency (%)
Bangkok 2487
51.3%
Phuket 2132
44.0%
Ko Pha-ngan 115
 
2.4%
Ko Phi Phi 111
 
2.3%
Koh Samui 1
 
< 0.1%

Length

2023-06-05T18:08:09.891211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T18:08:10.156669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
bangkok 2487
48.0%
phuket 2132
41.1%
ko 226
 
4.4%
phi 222
 
4.3%
pha-ngan 115
 
2.2%
koh 1
 
< 0.1%
samui 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
k 7106
21.8%
a 2718
 
8.3%
n 2717
 
8.3%
o 2714
 
8.3%
g 2602
 
8.0%
B 2487
 
7.6%
h 2470
 
7.6%
P 2469
 
7.6%
u 2133
 
6.5%
e 2132
 
6.5%
Other values (7) 3037
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26948
82.7%
Uppercase Letter 5184
 
15.9%
Space Separator 338
 
1.0%
Dash Punctuation 115
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
k 7106
26.4%
a 2718
 
10.1%
n 2717
 
10.1%
o 2714
 
10.1%
g 2602
 
9.7%
h 2470
 
9.2%
u 2133
 
7.9%
e 2132
 
7.9%
t 2132
 
7.9%
i 223
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
B 2487
48.0%
P 2469
47.6%
K 227
 
4.4%
S 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
338
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32132
98.6%
Common 453
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
k 7106
22.1%
a 2718
 
8.5%
n 2717
 
8.5%
o 2714
 
8.4%
g 2602
 
8.1%
B 2487
 
7.7%
h 2470
 
7.7%
P 2469
 
7.7%
u 2133
 
6.6%
e 2132
 
6.6%
Other values (5) 2584
 
8.0%
Common
ValueCountFrequency (%)
338
74.6%
- 115
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
k 7106
21.8%
a 2718
 
8.3%
n 2717
 
8.3%
o 2714
 
8.3%
g 2602
 
8.0%
B 2487
 
7.6%
h 2470
 
7.6%
P 2469
 
7.6%
u 2133
 
6.5%
e 2132
 
6.5%
Other values (7) 3037
9.3%

Interactions

2023-06-05T18:07:45.026099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:10.725571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:13.351381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:15.811506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:18.778151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:21.312320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:24.736771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:30.902484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:35.736320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:41.302315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:45.324575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:11.012422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:13.574864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:16.060533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:19.026792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:21.559724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:25.003366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:31.290186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:36.211425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:41.708970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:45.617337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:11.256003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:13.799645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:16.305003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:19.283084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:21.916142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:25.441896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:31.598963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:36.706811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:42.211977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:45.894614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:11.524279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:14.051675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:16.567281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:19.545388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:22.237441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:25.807922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:31.905137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:37.336342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:42.627510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:46.159678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:11.768371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:14.293197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:16.855552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:19.783385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:22.521690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:26.519372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:32.193408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:37.982615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:42.955512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:46.417571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:12.023278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:14.534007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:17.173129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:20.025928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:22.805367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:27.058451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:32.483656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:38.575907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:43.343003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:46.723433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:12.309269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:14.788811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:17.470940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:20.290308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:23.276786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:27.475346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:32.791520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:39.318156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:43.736051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:47.012810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:12.578852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:15.037376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:17.733145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:20.554379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:23.719462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:27.987453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:33.712147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:39.905261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:44.093993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:47.279561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:12.841681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:15.288545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:18.253654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:20.803225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:24.103872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:29.139749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:34.377011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:40.439586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:44.410660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:47.544700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:13.093957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:15.545995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:18.520264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:21.062475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:24.436052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:30.046052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:35.178146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:40.876831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T18:07:44.733150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-05T18:08:10.924705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
PriceStarsScoreReviewsBooked todayReal Guest Cleanlines ScoreReal Guest Facilities ScoreReal Guest Location ScoreReal Guest Service ScoreReal Guest Value for money ScoreSparkling cleanNewlyBuiltExcellentViewCheck In 24/7AirportTransferFront DeskValet ParkingFree WiFi In All RoomsSwimming PoolBarCoffeeDailyHousekeepingGolfKids clubOrigin
Price1.0000.3340.2550.0940.0340.2490.2900.1830.2300.1990.2110.0000.0950.0550.0870.0220.1480.0260.1860.1470.1910.0860.1020.2230.159
Stars0.3341.0000.0600.3940.2630.1070.154-0.0250.0950.0180.2440.0720.1560.3330.2810.3690.2560.1360.2910.3080.3100.4260.2130.3720.087
Score0.2550.0601.000-0.069-0.0330.8930.8750.6390.8400.8640.7470.0000.0930.1610.2190.2310.1020.1230.0950.1760.1700.2720.0930.1230.094
Reviews0.0940.394-0.0691.0000.570-0.060-0.0430.060-0.093-0.1070.0530.0000.1900.1080.0890.1160.0820.0000.0600.0840.1000.0650.0340.0820.048
Booked today0.0340.263-0.0330.5701.000-0.0080.0110.070-0.031-0.0510.0220.0300.1430.1440.0210.1560.0420.0260.0570.0480.0840.0980.0000.0800.100
Real Guest Cleanlines Score0.2490.1070.893-0.060-0.0081.0000.8940.5510.8590.8640.7620.0000.1040.1360.2290.1830.0930.1260.0810.1370.1720.2270.0790.1060.101
Real Guest Facilities Score0.2900.1540.875-0.0430.0110.8941.0000.5260.8260.8480.7290.0000.0840.1270.1810.1940.0950.1370.1270.1580.1800.2740.0800.1510.094
Real Guest Location Score0.183-0.0250.6390.0600.0700.5510.5261.0000.5330.5680.4180.0320.0640.1490.1570.1810.0920.1220.0570.1450.1630.2390.0710.0780.049
Real Guest Service Score0.2300.0950.840-0.093-0.0310.8590.8260.5331.0000.8080.6730.0310.0820.1190.2070.1760.0920.1250.0590.1410.1470.2280.0710.1080.109
Real Guest Value for money Score0.1990.0180.864-0.107-0.0510.8640.8480.5680.8081.0000.6400.0000.1050.1620.2080.2340.1020.1350.0790.1690.1690.3030.0790.1150.084
Sparkling clean0.2110.2440.7470.0530.0220.7620.7290.4180.6730.6401.0000.0180.1140.0700.1740.1350.0000.0000.0000.0420.0000.1180.0000.0000.101
NewlyBuilt0.0000.0720.0000.0000.0300.0000.0000.0320.0310.0000.0181.0000.0250.0400.0280.0000.0270.0270.0000.0000.0290.0290.0000.0170.000
ExcellentView0.0950.1560.0930.1900.1430.1040.0840.0640.0820.1050.1140.0251.0000.0270.0850.0640.0360.0000.0700.1440.1110.0810.0310.1170.258
Check In 24/70.0550.3330.1610.1080.1440.1360.1270.1490.1190.1620.0700.0400.0271.0000.1920.4470.1270.0780.0980.1920.1920.2450.1280.1670.151
AirportTransfer0.0870.2810.2190.0890.0210.2290.1810.1570.2070.2080.1740.0280.0850.1921.0000.2060.1790.0960.1410.2810.2620.2420.2070.1870.292
Front Desk0.0220.3690.2310.1160.1560.1830.1940.1810.1760.2340.1350.0000.0640.4470.2061.0000.1520.1100.0780.2610.2270.3560.1080.1660.176
Valet Parking0.1480.2560.1020.0820.0420.0930.0950.0920.0920.1020.0000.0270.0360.1270.1790.1521.0000.0000.0450.1840.1510.1360.1910.1720.094
Free WiFi In All Rooms0.0260.1360.1230.0000.0260.1260.1370.1220.1250.1350.0000.0270.0000.0780.0960.1100.0001.0000.0330.0830.0910.2620.0340.0000.082
Swimming Pool0.1860.2910.0950.0600.0570.0810.1270.0570.0590.0790.0000.0000.0700.0980.1410.0780.0450.0331.0000.1100.1090.0610.1580.0000.178
Bar0.1470.3080.1760.0840.0480.1370.1580.1450.1410.1690.0420.0000.1440.1920.2810.2610.1840.0830.1101.0000.4240.2600.1710.2240.178
Coffee0.1910.3100.1700.1000.0840.1720.1800.1630.1470.1690.0000.0290.1110.1920.2620.2270.1510.0910.1090.4241.0000.2320.1420.1700.078
DailyHousekeeping0.0860.4260.2720.0650.0980.2270.2740.2390.2280.3030.1180.0290.0810.2450.2420.3560.1360.2620.0610.2600.2321.0000.1300.1070.048
Golf0.1020.2130.0930.0340.0000.0790.0800.0710.0710.0790.0000.0000.0310.1280.2070.1080.1910.0340.1580.1710.1420.1301.0000.1650.164
Kids club0.2230.3720.1230.0820.0800.1060.1510.0780.1080.1150.0000.0170.1170.1670.1870.1660.1720.0000.0000.2240.1700.1070.1651.0000.172
Origin0.1590.0870.0940.0480.1000.1010.0940.0490.1090.0840.1010.0000.2580.1510.2920.1760.0940.0820.1780.1780.0780.0480.1640.1721.000

Missing values

2023-06-05T18:07:47.991140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-05T18:07:49.589224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NamePriceStarsScoreSpokenLanguagesReviewsLocationSparkling cleanNewlyBuiltExcellentViewCheck In 24/7AirportTransferFront DeskValet ParkingFree WiFi In All RoomsSwimming PoolBarCoffeeDailyHousekeepingGolfKids clubBooked todayReal Guest Cleanlines ScoreReal Guest Facilities ScoreReal Guest Location ScoreReal Guest Service ScoreReal Guest Value for money ScoreOrigin
0Asia Hotel Bangkok (SHA Plus+)1194.07.9English, Thai27771296 Phayathai Road, Siam, Bangkok, Thailand, 104000011010🌐111100176.07.67.59.17.57.6Bangkok
1Rembrandt Hotel & Suites (SHA Plus+)894.58.3English, Thai600119 Sukhumvit Soi 18, Klong Toei, Sukhumvit, Bangkok, Thailand, 101100011111🌐01110032.08.58.18.48.38.6Bangkok
2Dream Hotel Bangkok (SHA Plus+)774.58.4English, Thai1610910 Sukhumvit Soi 15, Sukhumvit, Bangkok, Thailand, 101100001111🌐11111031.08.88.18.58.48.7Bangkok
3VIX Bangkok @ Victory Monument683.09.2English, Chinese [Mandarin], Thai143913-15 Thanon Ratchawithi, Chatuchak, Bangkok, Thailand, 104001000010🌐00110024.09.39.09.59.49.3Bangkok
4The Berkeley Hotel Pratunam (SHA Plus+)2385.08.2English, Chinese [Mandarin], Thai61617559 Ratchathewi, Pratunam, Bangkok, Thailand, 104000001110🌐011100128.08.38.18.88.28.1Bangkok
5Ambassador Hotel Bangkok (SHA Plus+)1204.07.2English, Thai22016171 Sukhumvit Rd., Soi 11, Wattana, Sukhumvit, Bangkok, Thailand, 101100011010🌐11110099.07.06.98.46.86.9Bangkok
6Grand President Bangkok (SHA Plus+)1334.07.1English, Chinese [Mandarin], Thai1202716 Sukhumvit Soi 11 , Sukhumvit, Bangkok, Thailand, 101100001111🌐11110018.06.86.78.37.17.0Bangkok
7Solitaire Bangkok Sukhumvit 11834.58.2English, Thai923175/23 Soi Sukhumvit 13 Sukhumvit Road, Klongtoey-Nua, Wattana, Sukhumvit, Bangkok, Thailand, 101100011111🌐11110025.08.68.18.18.58.2Bangkok
8Grand 5 Hotel & Plaza Sukhumvit (SHA Extra Plus)824.07.9English, Thai300487 Sukhumvit Road Soi 5, Klongtoey Nua, Wattana, Bangkok, Sukhumvit, Bangkok, Thailand, 101100001010🌐01110025.08.17.38.67.97.6Bangkok
9Mövenpick Hotel Sukhumvit 15 Bangkok965.08.3English, Thai3768Soi Sukhumvit 15, Sukhumvit, Bangkok, Thailand, 101100001111🌐11110034.08.78.27.98.48.6Bangkok
NamePriceStarsScoreSpokenLanguagesReviewsLocationSparkling cleanNewlyBuiltExcellentViewCheck In 24/7AirportTransferFront DeskValet ParkingFree WiFi In All RoomsSwimming PoolBarCoffeeDailyHousekeepingGolfKids clubBooked todayReal Guest Cleanlines ScoreReal Guest Facilities ScoreReal Guest Location ScoreReal Guest Service ScoreReal Guest Value for money ScoreOrigin
4836Villa Paradiso8065.07.9English, Thai1Villa E, Malaiwana Estate,28/12 Moo 4, Tambon Sakoo, Amphur Thalang, Naithon, Phuket, Thailand, 831100000110🌐0001100.05.07.57.57.510.0Phuket
4837Montana Hotel & Hostel Phuket703.09.6English, Thai1528/27 Patak Road, Karon, Phuket 83100, Karon, Phuket, Thailand, 831001000110🌐0001000.010.07.510.010.010.0Phuket
4838Chomdao@Maikhao554.07.5English9Rural Road Phuket 3033, Mai Khao, Phuket, Thailand, 831100000100🌐0000000.06.06.02.06.02.0Phuket
4839Sealord Naithon Beachfront Villa1100.08.9English, Thai3131/10 Naithon Beach Road, Naithon, Phuket, Thailand, 831101000010🌐0111000.09.59.29.39.48.7Phuket
4840Living Room Guesthouse & Cafe Bar3311.09.4English, Thai44516/6 Soi Centara, Muang, Phuket, Karon, Phuket, Thailand, 831001000000🌐0111000.09.69.09.79.19.7Phuket
4841The Beach by Glitter House343.09.5English, Thai40110/54 Kata Road, Kata, Phuket, Thailand, 831001000100🌐1011000.09.49.69.49.59.4Phuket
4842Village House CAC1231042.09.6English, French, Thai6158/12 Soi Bang Thao 7, Moo5, Bangtao, Surin, Phuket, Thailand, 831101000100🌐0001000.09.79.79.010.09.7Phuket
4843The Beach by Glitter House1193.09.5English, Thai40110/54 Kata Road, Kata, Phuket, Thailand, 831001000100🌐1011000.09.49.69.49.59.4Phuket
4844Westkey Kamala villa8435.09.5English, Thai3Kamala, Phuket, Thailand1001010❌🌐1010000.09.39.39.310.09.3Phuket
4845Bcollection Resort1985.09.6English, Thai1Layan, Phuket, Thailand1000000❌🌐1010000.010.08.010.010.010.0Phuket